Simulating a local emissions levy to fund local energy effiency retrofit
Focus on: All English LSOAs
Ben Anderson
Last run at: 2021-10-20 13:28:36
1 About
Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
2 Citation
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom
3 Introduction
Background blurb about emissions, retofit, carbon tax/levy etc
4 Emissions Levy Case Study - All LSOAs
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.
NB: no maps in the interests of speed
4.1 Data
We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
- CREDS place-based emissions estimates (contains data from a wide range of sources including https://epc.opendatacommunities.org/ for EPCs)
- 2018 residential gas & electricity use data
- Indices of Deprivation 2019
- Urban/Rural classification at LSOA level
- various LSOA look-up tables from geoportal
All analysis is at LSOA level. Cautions on inference from area level data apply.
4.2 CREDS place-based emmissions estimates
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
- Emissions are presented as per capita…
- Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
## region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: <NA> 805 9834.584 2737.760
## 2: East 3392 8886.285 3133.982
## 3: East Midlands 2713 7835.809 3195.006
## 4: London 4826 9116.160 3137.503
## 5: North East 1634 6801.016 4123.301
## 6: North West 4463 7430.665 2862.846
## 7: South East 5278 9813.213 3648.334
## 8: South West 3059 7930.219 2780.179
## 9: West Midlands 3403 7506.665 4037.705
## 10: Yorkshire and The Humber 3271 7419.184 2772.234Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A Riverside 3373 3175 3110
## 2: Test Valley 003B St Mary's 2641 2487 2230
## 3: Milton Keynes 017H Broughton 2517 2382 2460
## 4: Test Valley 003A Alamein 2513 2638 2350
## 5: Peterborough 019D Stanground South 2261 2178 1880
## 6: Swindon 008B Blunsdon and Highworth 2227 2166 2020## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 013G Stratford and New Town 731 6351 6350
## 2: Wandsworth 002B Queenstown 675 3282 1700
## 3: Aylesbury Vale 012A Riverside 3373 3175 3110
## 4: Newham 037E Royal Docks 574 3116 2900
## 5: Lewisham 012E Lewisham Central 568 2893 2730
## 6: Test Valley 003A Alamein 2513 2638 2350## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A Riverside 3373 3175 3110
## 2: Test Valley 003B St Mary's 2641 2487 2230
## 3: Milton Keynes 017H Broughton 2517 2382 2460
## 4: Test Valley 003A Alamein 2513 2638 2350
## 5: Peterborough 019D Stanground South 2261 2178 1880
## 6: Swindon 008B Blunsdon and Highworth 2227 2166 2020Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
4.2.1 Estimate per dwelling emissions
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values| Name | …[] |
| Number of rows | 32039 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 19490.24 | 9188.71 | 3587.62 | 12947.16 | 18275.82 | 24069.71 | 586372.22 | ▇▁▁▁▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2465.42 | 851.99 | 3.92 | 2037.62 | 2434.68 | 2868.68 | 71095.56 | ▇▁▁▁▁ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1021.63 | 220.17 | 40.55 | 888.82 | 977.15 | 1092.44 | 4046.23 | ▂▇▁▁▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3487.05 | 912.74 | 458.61 | 2978.41 | 3398.85 | 3894.92 | 72698.53 | ▇▁▁▁▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 175.08 | 336.37 | 0.00 | 40.20 | 69.74 | 136.09 | 6877.09 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3662.13 | 910.23 | 912.57 | 3125.71 | 3558.65 | 4082.50 | 76436.03 | ▇▁▁▁▁ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 2200.93 | 1038.37 | 127.70 | 1529.01 | 2142.16 | 2797.44 | 89700.00 | ▇▁▁▁▁ |
| CREDSvan_kgco2e2018_pdw | 1 | 1 | 366.16 | 2774.12 | 0.05 | 137.01 | 217.71 | 342.60 | 344822.80 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 1 | 1 | 2567.13 | 2987.05 | 141.80 | 1742.05 | 2422.45 | 3151.56 | 346819.80 | ▇▁▁▁▁ |
Examine patterns of per dwelling emissions for sense.
4.2.1.1 All emissions
Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -123.51, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5753081 -0.5604715
## sample estimates:
## cor
## -0.5679359## LSOA11CD WD18NM All_Tco2e_per_dw
## Length:32039 Length:32039 Min. : 3.588
## Class :character Class :character 1st Qu.: 12.947
## Mode :character Mode :character Median : 18.276
## Mean : 19.490
## 3rd Qu.: 24.070
## Max. :586.372## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01031998 Durrington and Larkhill 586.3722
## 2: E01009320 Sheldon 364.6687
## 3: E01033484 Park East 203.6630
## 4: E01010151 Knowle 171.2150
## 5: E01019556 Holmebrook 160.1703
## 6: E01033749 Greenbank 139.6909## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01004562 Queenstown 4.965387
## 2: E01005133 Ancoats & Beswick 4.906386
## 3: E01008703 Hendon 4.369222
## 4: E01015895 Victoria 4.289301
## 5: E01033726 Eltham West 3.808630
## 6: E01033583 Stratford and New Town 3.5876244.2.1.2 Home energy use
Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.92 2037.62 2434.68 2465.42 2868.68 71095.56## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -70.089, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3740796 -0.3550910
## sample estimates:
## cor
## -0.3646232Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4069829 -0.3885483
## sample estimates:
## cor
## -0.3978058Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4069829 -0.3885483
## sample estimates:
## cor
## -0.3978058## RUC11 mean_gas_kgco2e mean_elec_kgco2e
## 1: Rural town and fringe 2536.798 1083.0125
## 2: Rural town and fringe in a sparse setting 2254.050 993.6811
## 3: Rural village and dispersed 1879.326 1481.8790
## 4: Rural village and dispersed in a sparse setting 1015.146 1405.2387
## 5: Urban city and town 2456.035 991.9263
## 6: Urban city and town in a sparse setting 2230.231 945.0026
## 7: Urban major conurbation 2552.187 981.2844
## 8: Urban minor conurbation 2582.837 913.8924
## mean_other_energy_kgco2e
## 1: 274.22605
## 2: 271.63854
## 3: 1131.91956
## 4: 1440.13693
## 5: 86.29202
## 6: 124.64526
## 7: 108.70527
## 8: 123.97196Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 158.14, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6559143 0.6682142
## sample estimates:
## cor
## 0.6621088## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 177.83, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6992585 0.7102801
## sample estimates:
## cor
## 0.7048118## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
How does the correlation look now?
4.2.1.3 Transport
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -119.05, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5613723 -0.5461891
## sample estimates:
## cor
## -0.5538267## RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Rural town and fringe 2882.600 412.7957
## 2: Rural town and fringe in a sparse setting 2198.057 346.9746
## 3: Rural village and dispersed 3754.901 664.4004
## 4: Rural village and dispersed in a sparse setting 3095.886 586.5956
## 5: Urban city and town 2280.407 379.2851
## 6: Urban city and town in a sparse setting 1761.591 300.1992
## 7: Urban major conurbation 1718.983 NA
## 8: Urban minor conurbation 1899.379 307.5766Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = -0.79155, df = 32036, p-value = 0.4286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.015371712 0.006528074
## sample estimates:
## cor
## -0.0044223494.2.2 Impute EPC counts
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.0 315.0 390.0 434.2 503.0 6350.0## N elec meters## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 36.0 623.0 692.0 736.3 809.0 6351.0Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
4.2.3 Estimating the annual emissions levy
Case studies:
- Annual carbon tax
- Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
- September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind
- based on a Marginal Abatement Cost (MAC)
- 2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
4.2.3.1 Scenario 1: Central cost
The table below shows the overall £ GBP total for the case study area in £M.
## £m## nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: 32039 107100.4 13847.3 5871.4## £m## region nLSOAs beis_GBPtotal_c beis_total_c_gas
## 1: South East 5278 20772.109 2272.0667
## 2: London 4826 19038.076 2048.9049
## 3: North West 4463 12703.630 1952.4218
## 4: East 3392 12199.273 1450.0085
## 5: West Midlands 3403 10191.715 1475.8156
## 6: South West 3059 9784.332 1147.1720
## 7: Yorkshire and The Humber 3271 9495.299 1494.0830
## 8: East Midlands 2713 8687.056 1249.5031
## 9: North East 1634 4228.912 757.3235
## beis_GBPtotal_c_elec
## 1: 1038.8330
## 2: 870.4882
## 3: 766.0786
## 4: 668.5684
## 5: 604.8308
## 6: 613.8662
## 7: 550.1641
## 8: 503.9008
## 9: 254.6698
The table below shows the mean per dwelling value rounded to the nearest £10.
## beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1: 4780 600 250
## beis_GBPtotal_c_energy_perdw
## 1: 850Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.7: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.8: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 879 3172 4478 4775 5897 143661## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw
## 1: E01031998 Wiltshire 045C Durrington and Larkhill 586372.2
## 2: E01009320 Birmingham 081F Sheldon 364668.7
## 3: E01033484 Darlington 008F Park East 203663.0
## 4: E01010151 Solihull 026A Knowle 171215.0
## 5: E01019556 Chesterfield 010C Holmebrook 160170.3
## 6: E01033749 Liverpool 042F Greenbank 139690.9
## beis_GBPtotal_c_perdw
## 1: 143661.19
## 2: 89343.84
## 3: 49897.44
## 4: 41947.69
## 5: 39241.73
## 6: 34224.27## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw
## 1: E01004562 Wandsworth 002B Queenstown 4965.387
## 2: E01005133 Manchester 013D Ancoats & Beswick 4906.386
## 3: E01008703 Sunderland 013B Hendon 4369.222
## 4: E01015895 Southend-on-Sea 010A Victoria 4289.301
## 5: E01033726 Greenwich 034E Eltham West 3808.630
## 6: E01033583 Newham 013G Stratford and New Town 3587.624
## beis_GBPtotal_c_perdw
## 1: 1216.5198
## 2: 1202.0645
## 3: 1070.4593
## 4: 1050.8787
## 5: 933.1143
## 6: 878.9679Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.96 499.22 596.50 604.03 702.83 17418.41## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw
## 1: E01031998 Wiltshire 045C Durrington and Larkhill 71.095556
## 2: E01000213 Barnet 033F Garden Suburb 7.355417
## 3: E01023812 Three Rivers 004A Chorleywood North & Sarratt 7.167803
## 4: E01023841 Three Rivers 011C Moor Park & Eastbury 6.925828
## 5: E01004114 Sutton 025D Cheam 6.721036
## 6: E01023813 Three Rivers 004B Chorleywood North & Sarratt 6.718669
## beis_GBPtotal_c_gas_perdw
## 1: 17418.411
## 2: 1802.077
## 3: 1756.112
## 4: 1696.828
## 5: 1646.654
## 6: 1646.074## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw
## 1: E01026645 King's Lynn and West Norfolk 002A Brancaster 0.015725987
## 2: E01026718 King's Lynn and West Norfolk 004D Valley Hill 0.014207424
## 3: E01027382 Northumberland 002D Norham and Islandshires 0.013286252
## 4: E01020864 County Durham 064G Evenwood 0.013174354
## 5: E01032746 Southampton 029F Bargate 0.012818095
## 6: E01020534 West Dorset 003F Maiden Newton 0.003918875
## beis_GBPtotal_c_gas_perdw
## 1: 3.8528667
## 2: 3.4808188
## 3: 3.2551318
## 4: 3.2277167
## 5: 3.1404333
## 6: 0.9601244Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.934 217.760 239.403 250.299 267.648 991.327## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01000206 Barnet 033B Garden Suburb 4.046235 991.3275
## 2: E01030692 Runnymede 005D Virginia Water 3.360000 823.2000
## 3: E01030342 Elmbridge 018B Oxshott and Stoke D'Abernon 3.346058 819.7842
## 4: E01030346 Elmbridge 016A Weybridge St George's Hill 3.280690 803.7690
## 5: E01004690 Westminster 019D Knightsbridge and Belgravia 2.875978 704.6145
## 6: E01003465 Merton 002D Village 2.873194 703.9325## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01008777 Sunderland 026C St Chad's 0.50798472 124.456256
## 2: E01024604 Swale 014C St Ann's 0.48269076 118.259237
## 3: E01002862 Kensington and Chelsea 014E Stanley 0.45468354 111.397468
## 4: E01033736 Greenwich 004H Woolwich Riverside 0.43406378 106.345626
## 5: E01004739 Westminster 024E Tachbrook 0.33211144 81.367302
## 6: E01010257 Walsall 007E Aldridge North and Walsall Wood 0.04054826 9.934324Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 112.4 729.7 832.7 854.3 954.3 17811.14.2.3.2 Scenario 2: Rising block tariff
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw## 0% 25% 50% 75% 100%
## 3587.624 12947.165 18275.816 24069.709 586372.222## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
## 1: 20.14367 1579.554 1305.5194 685.5016 3570.575
## 2: 18.65717 1579.554 1305.5194 139.9562 3025.030
## 3: 12.73055 1553.127 0.0000 0.0000 1553.127
## 4: 19.87204 1579.554 1305.5194 585.8127 3470.886
## 5: 28.94094 1579.554 1305.5194 3914.1019 6799.175
## 6: 15.12282 1579.554 533.0367 0.0000 2112.591
## 7: 20.44254 1579.554 1305.5194 795.1884 3680.262
## 8: 21.00183 1579.554 1305.5194 1000.4491 3885.523
## 9: 11.91349 1453.446 0.0000 0.0000 1453.446
## 10: 27.68047 1579.554 1305.5194 3451.5071 6336.581| Name | …[] |
| Number of rows | 32039 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 19.49 | 9.19 | 3.59 | 12.95 | 18.28 | 24.07 | 586.37 | ▇▁▁▁▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3727.91 | 3031.87 | 437.69 | 1579.54 | 2885.00 | 5011.43 | 211376.45 | ▇▁▁▁▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2528423.89 | 1653321.47 | 543095.20 | 1222262.12 | 2220433.98 | 3356216.64 | 84377314.16 | ▇▁▁▁▁ |
## nLSOAs sum_total_sc1 sum_total_sc2
## 1: 32039 107100.4 81008.17
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1: 130.9338 15.97393
## 2: 82.7677 10.09766
## 3: 717.3671 87.51878
## 4: 1041.0619 127.00956
## 5: 2480.0943 248.59012
## 6: 793.2446 96.77584## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw beis_GBPgas_sc2_h_perdw
## 1: 130.9338 15.97393 0.00000 0.00000
## 2: 82.7677 10.09766 0.00000 0.00000
## 3: 717.3671 87.51878 0.00000 0.00000
## 4: 1041.0619 127.00956 0.00000 0.00000
## 5: 2480.0943 248.59012 97.27961 16.66576
## 6: 793.2446 96.77584 0.00000 0.00000
## beis_GBPgas_sc2_perdw
## 1: 15.97393
## 2: 10.09766
## 3: 87.51878
## 4: 127.00956
## 5: 362.53549
## 6: 96.77584## [1] 9086.681
## [1] 3997.045
## £m## nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: 32039 81008.17 9086.681 3997.045 54619583## £m## region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: South East 5278 17507.695 1503.8874 766.6684 8973952
## 2: London 4826 15649.748 1389.3724 583.7890 8889572
## 3: East 3392 9526.267 939.0531 487.3759 5818700
## 4: North West 4463 8777.331 1282.6361 491.5296 7236660
## 5: West Midlands 3403 7270.355 976.6076 410.8372 5765703
## 6: South West 3059 6809.964 653.9993 431.0802 5213266
## 7: Yorkshire and The Humber 3271 6504.827 1011.0844 342.8749 5405939
## 8: East Midlands 2713 6247.711 827.1231 339.4664 4693551
## 9: North East 1634 2714.274 502.9172 143.4232 2622240
4.2.4 Estimate retofit costs
- from A-E <- £13,300
- from F-G <- £26,800
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
## To retrofit D-E (£m)## [1] 177847.9## Number of dwellings: 13372024## To retrofit F-G (£m)## [1] 26752.52## Number of dwellings: 998229## To retrofit D-G (£m)## [1] 204600.4## To retrofit D-G (mean per dwelling)## [1] 14163.45## meanPerLSOA_GBPm total_GBPm
## 1: 6.385981 204600.4## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
4.2.5 Compare levy with costs
4.2.5.1 Scenario 1
Totals
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
4.2.5.2 Scenario 2
Totals
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
4.2.6 Years to pay…
4.2.6.1 Scenario 1
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.09599 2.40262 3.17210 3.53055 4.44332 15.64765 1## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.7742 14.7584 16.8635 17.5709 19.2684 118.6634 1## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Highest retofit sum cost## LSOA11CD LSOA11NM WD18NM retrofitSum yearsToPay epc_D_pc epc_E_pc
## 1: E01019012 Cornwall 054E St Ives East 26389383 32.33181 0.2881890 0.2251969
## 2: E01018781 Cornwall 034B Rame Peninsular 22060172 49.74697 0.2993730 0.2664577
## 3: E01027840 Scarborough 002C Mulgrave 21959636 32.73446 0.2821317 0.2272727
## 4: E01021988 Tendring 018A Golf Green 21701517 36.61775 0.2955900 0.3313468
## 5: E01018766 Cornwall 028D Looe West, Lansallos and Lanteglos 21409249 46.57111 0.2181070 0.2716049
## 6: E01020541 West Dorset 002C Sherborne East 21066562 31.46956 0.3038793 0.3232759
## 7: E01026741 North Norfolk 004A High Heath 20793004 35.15440 0.2971888 0.2650602
## 8: E01019002 Cornwall 070B Newlyn and Mousehole 20415414 45.01341 0.1710963 0.2807309
## 9: E01018982 Cornwall 057C Hayle North 20411151 40.50980 0.2675386 0.1819263
## 10: E01027374 Northumberland 003A Bamburgh 19563519 29.95735 0.3329532 0.2257697
## epc_F_pc epc_G_pc
## 1: 0.1314961 0.07086614
## 2: 0.2335423 0.10971787
## 3: 0.2163009 0.10031348
## 4: 0.1620977 0.14302741
## 5: 0.2935528 0.10973937
## 6: 0.2090517 0.06896552
## 7: 0.1994645 0.06827309
## 8: 0.3089701 0.17940199
## 9: 0.2092747 0.14030916
## 10: 0.1402509 0.05131129
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
4.2.6.2 Scenario 2
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.06524 2.83119 4.88954 5.92627 8.83376 31.42356 1## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.7742 14.7584 16.8635 17.5709 19.2684 118.6634 1## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
What happens in Year 2 totally depends on the rate of upgrades…
4.2.6.3 Compare scenarios
Comparing pay-back times for the two scenarios - who does the rising block tariff help?
x = y line shown for clarity
5 R environment
5.1 R packages used
- base R (R Core Team 2016)
- bookdown (Xie 2016a)
- data.table (Dowle et al. 2015)
- ggplot2 (Wickham 2009)
- kableExtra (Zhu 2018)
- knitr (Xie 2016b)
- rmarkdown (Allaire et al. 2018)
- skimr (Arino de la Rubia et al. 2017)
5.2 Session info
6 Data Tables
I don’t know if this will work…
## Doesn't